spectrum 0.6.8

Spectrum Analysis Tools

SPECTRUM : Spectral Analysis in Python

contributions:

Please join https://github.com/cokelaer/spectrum

contributors:

https://github.com/cokelaer/spectrum/graphs/contributors

issues:

Please use https://github.com/cokelaer/spectrum/issues

documentation:

http://pyspectrum.readthedocs.io/

Spectrum contains tools to estimate Power Spectral Densities using methods based on Fourier transform, Parametric methods or eigenvalues analysis:

• The Fourier methods are based upon correlogram, periodogram and Welch estimates. Standard tapering windows (Hann, Hamming, Blackman) and more exotic ones are available (DPSS, Taylor, …).

• The parametric methods are based on Yule-Walker, BURG, MA and ARMA, covariance and modified covariance methods.

• Non-parametric methods based on eigen analysis (e.g., MUSIC) and minimum variance analysis are also implemented.

• Multitapering is also available

The targetted audience is diverse. Although the use of power spectrum of a signal is fundamental in electrical engineering (e.g. radio communications, radar), it has a wide range of applications from cosmology (e.g., detection of gravitational waves in 2016), to music (pattern detection) or biology (mass spectroscopy).

Installation

pip install spectrum

For Linux and MAC users, if you prefer to use conda (avoiding

compilation of dependencies), please use:

conda config --add channels conda-forge
conda install spectrum

To install the conda executable, please see https://www.continuum.io/downloads .

Contributions

Please see github for any issues/bugs/comments/contributions.

Some notebooks (external contributions)

• http://nbviewer.ipython.org/gist/juhasch/5182528